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Background: The neuro-endocrino-immunological (NEI) pathophysiology associated with advanced heart failure (AdHF) progression and arrythmogenesis is not well understood. While it has been shown that mechanical circulatory support device (MCSD) implantation rapidly restores normal hemodynamics, it is unknown as to whether NEI mechanisms also normalize rapidly. We hypothesized that transcriptional activity in PBMC reflecting NEI normalizes shortly after MCSD implantation. Methods: We collected PBMC samples from 25 MCSD patients and 4 healthy controls (CTRL). Samples were obtained at 1 day before, and 1, 3, 5 and 8 days after MCSD. Purified mRNA was subjected to whole-genome NGS analysis. Statistically significant genes, dysregulated between AdHF patients and CTRLs, were subjected to time dependent bioinformatics analysis. Results: We identified 1226 dysregulated gene-transcripts between AdHF patients and CRTLs at baseline. Time dependent analysis provided 344 dysregulated transcripts in AdHF patients across all time-points. An analysis of enriched molecular pathways by dysregulated genes showed 52 pathways with NEI annotations at baseline. The Endothelin pathway, one of the most significant of the 52 pathways, had 17/60 dysregulated transcripts in AdHF patients at baseline and through day 8 after implantation (Fig.1A). Within the Endothelin pathway we found that expression of EDNRB, a gene central to ET1 clearance and ECE-inhibition, is persistently elevated through day 8 after MCSD (Fig.1B). Conclusion: Transcriptional NEI dysregulation is persistent in PBMC 8 days after MCSD implantation. Neuromodulatory strategies targeted at NEI dysregulation and implemented after MCSD implantation may be a mechanism to improve perioperative outcomes. Further studies including validation methods to objectively assess NEI activity are warranted.

Background: Endomyocardial Biopsy ( EMB) is the standard method to diagnose allograft rejection post HTx. While it is used to support medical decisions, insufficient diagnostic accuracy constitutes a fundamental limitation. The aim of this study is to develop a methodology that improves the classification of the EMB through a non-supervised evaluation of intramyocardial gene expression. Methods: Sixty-four heart tissues from 47 HTx recipients were subjected to genome wide mRNA sequencing. An unsupervised algorithm using optimal transport to mitigate batch effects and to filter confounding sources of variability was developed to identify molecular signatures of rejection. Linear Mixed Model identified genes statistically significant among the histology defined rejection groups. Weighted Gene Correlation Network Analysis (WGCNA) was used to establish 13 eigengene modules and module-clinical phenotype relationships. Gene Ontology was used for interpretation of the modules in their biological context. Results: O ur algorithm best classified the EMBs into 4 unsupervised clusters solely based on their gene expression. Statistical analysis showed a set of genes differentially expressed among groups defined by histology criteria. Top ranked genes were CLNK, TNFRSF10A, TRADD, CD2, and HLA-A. WGCNA revealed best trait-module correlation was observed between the classes defined by the unsupervised algorithm developed in this study followed by Histology. Figure 1 shows Module-Trait relationships, strength of association, significance and enriched biological process. Conclusion: We have developed an unsupervised algorithm that classifies the EMBs into 4 functionally distinctive categories. These categories are highly correlated with genomic modules defined by WGCNA and with the clinical phenotypes. To our knowledge, this is the first unsupervised classification of the EMBs. Further validation and performance will be provided at the time of presentation.

Introduction Endomyocardial biopsy is the standard surveillance method to detect cardiac allograft rejection. While microRNAs (miRNA) play a major role in regulating mRNA, their nature and role in the biology is not well understood. We hypothesized that specific mRNA-miRNA networks can be identified underlying the clinical phenotypes of different forms of cardiac allograft rejection. Method Twenty one tissue samples from 14 post-HTx patients were subjected to genome wide miRNA sequencing. A non-parametric empirical Bayes framework removed batch effect and filtered genes with low variability. Weighted Gene Correlation Network Analysis (WGCNA) clustered genes into related eigengene modules based on their gene expression. Identified miRNAs were subjected to target prediction and compared with mRNA expression profiles previously identified on the same biopsies. Gene Ontology (GO) was used for biological interpretation of selected genes. Results 1270 miRNAs were used to construct 9 eigengene modules. Module-Trait relationship were then investigated as shown in Figure. The top ten miRNA probe sets filtered by the highest intra-module correlation and statistical significance were hsa-miR-141-3p, hsa-miR-150-5p, hsa-miR-605, hsa-miR-582-5p, hsa-miR-3150b-3p, hsa-miR-508-3p, hsa-miR-652-5p, hsa-miR-26a-1-3p, hsa-miR-3667-3p and hsa-miR-3911. Target prediction analysis resulted in 724 gene targets. GO analysis revealed 184 categories enriched by these genes including regulation of protein kinase activity, cardiac muscle cell differentiation and epithelial cell migration among others. Compared to mRNA previously identified in the same heart biopsies showed 685 overlapping gene targets. Conclusion WGCNA identified miRNA modules correlated with different clinical phenotypes of rejection. MRNA-miRNA pairs were identified to help understand the biology of rejection and as interesting candidates for diagnostic or therapeutic applications.